🎵 TuneWeave: Curate Your Unique Sonic Journey 🎧

“Crafting your musical soulmate with personalized, data-driven recommendations.”

TuneWeave is a modern music discovery application that generates personalized playlists with minimal effort. It analyzes listening preferences and leverages large-scale playlist data to recommend tracks and assemble bespoke playlists that evolve with your taste.

📚 Table of Contents

• Overview
• Features
• Dataset
• Quick Start
• Local Development
• Project Structure
• Contributing
• License

🚀 Overview

Keeping up with new releases and finding hidden gems is hard. TuneWeave bridges the gap with a personalized discovery experience. By combining preference modeling with large-scale playlist patterns, it delivers high-quality recommendations and auto-generated playlists tailored to you.

✨ Features

• Personalized suggestions: Preference-aware track and artist recommendations.
• Playlist generation: Build playlists by mood, genre, or seed artists.
• Trend discovery: Surface popular tracks across genres.
• Containerized runtime: Run locally via Docker in minutes.

📂 Dataset

TuneWeave uses the Spotify Million Playlist Dataset, a large-scale corpus of user playlists for training and evaluation.

• Dataset: Spotify Million Playlist Dataset
• Place .json files under producer/data/ before running the app

Example snippet (abridged):

{
  "name": "musical",
  "pid": 5,
  "num_tracks": 12,
  "tracks": [
    {
      "pos": 0,
      "artist_name": "Degiheugi",
      "track_name": "Finalement",
      "duration_ms": 166264
    }
  ]
}

🧪 Quick Start

Prerequisites:

• Install Docker: https://www.docker.com/get-started

Clone the repository:

git clone https://github.com/Mexicoco/TuneWeave.git
cd TuneWeave

Add dataset files:

• Download the full dataset from the link above and place the .json files under producer/data/.

Build and run:

docker-compose build
docker-compose up

Open the app:

• Navigate to http://localhost:80 in your browser.

🔧 Local Development

• Ensure producer/data/ contains the dataset .json files.
• Use Docker for a consistent environment. If you prefer running services individually, mirror the container configuration locally.

🗂 Project Structure

High-level layout (key folders):

• producer/ — data ingestion/processing and dataset files under producer/data/
• docker-compose.yml — multi-service orchestration for local runtime
• proj/ — project documentation and meta files

🤝 Contributing

Contributions are welcome! Please open an issue or submit a pull request.

1. Fork the repo on GitHub
2. Create a feature branch
3. Commit changes with clear messages
4. Open a pull request with context and screenshots if applicable

📄 License

This project is licensed under the MIT License. See LICENSE for details.

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Maintained by @GinaGWL — GitHub: https://github.com/GinaGWL